The Centennial Variation of El NinoImpact on Atlantic TropicalCyclonesRuixin Yanga

Department of Geography and Geoinformation Science, College of Science, and Centerfor Earth Observing and Space Research, George Mason University, Fairfax, Virginia

Allison Fairley

Department of Geography and Geoinformation Science, College of Science, GeorgeMason University, Fairfax, Virginia

Wonsun Park

GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany

Received 4 April 2017; in final form 24 October 2017

ABSTRACT: Predicting tropical cyclone (TC) activity becomes more im-portant every year while the understanding of what factors impact them con-tinues to be complicated. El Nino–Southern Oscillation (ENSO) is one of theprimary factors impacting the activities in both the Pacific and the Atlantic, butan extensive examination of the fluctuation in this system has yet to be studiedin its entirety. This article analyzes the ENSO impacts on the Atlantic tropicalcyclone activity during the assessed warm and cold years to show the dominantcentennial-scale variation impact. This study looks to plausibly link this

aCorresponding author: Ruixin Yang, ryang@gmu.edu

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 1

DOI: 10.1175/EI-D-17-0006.1

� 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy(www.ametsoc.org/PUBSReuseLicenses).

variation to the Southern Ocean centennial variability, which is rarely men-tioned in any factors affecting the Atlantic tropical cyclone activity. This cen-tennial variability could be used to enhance future work related to predictingtropical cyclones.

KEYWORDS: Tropical cyclones; Atmosphere2ocean interaction; Climatevariability; El Nino; ENSO; Time series

1. IntroductionTropical cyclones (TCs) in general, or hurricanes [TCs with wind speed higher

than 63 kt (1 kt 5 0.51m s21)] in particular, are of significant interest to the sci-entific community as well as the general public. Further understanding of whatcauses TCs to develop, intensify, and change paths will continue to be of interestto the general public as TCs continue to impact a large portion of the worldpopulation. In an era of knowledge sharing and accessibility, continuing to de-velop and aggregate information to make decisions is very important. Sheddinglight on what could cause shifts in TC patterns (short or long scale) and furtherenhancing the scientific community’s ability to understand the inner workings ofthe TCs will enable the public to further understand TCs and make better deci-sions such as locations of vacations and new homes. In addition to the short-timeTC forecasting as part of weather forecasting (e.g., real-time tropical weatherforecasting at http://www.nhc.noaa.gov/), seasonal forecasting also attracts theattention of the TC researchers. Among most seasonal TC forecasting schemes,the climate variability associated with the so-called El Nino and La Nina phenomenaplays an important role.

The indicator of El Nino or La Nina is the central and eastern Pacific sea surfacetemperature (SST) fluctuation. When the 3-month average SST anomaly (SSTA)over the Nino-3.4 region (58N–58S, 1208–1708W) is higher than 0.58C in fiveconsecutive months, a warm episode, or El Nino, takes place. The opposite coldepisode, or La Nina, corresponds to SSTA being less than 20.58C in the samecondition [Climate Prediction Center (CPC) 2015].

Complimentary to these anomalous SST values, extreme fluctuations of atmo-spheric pressure at sea level are also identified throughout the Pacific Ocean, which iscommonly known as the Southern Oscillation. These variations from the norm be-tween the western and eastern Pacific are described by the Southern Oscillation index(SOI). The variation of the Pacific SST and the fluctuation of the pressure differenceare highly correlated, and they are together called the El Nino–Southern Oscillation(ENSO). The periods of large negative values of SOI correspond to warm episodes inthe eastern Pacific (Philander 1990; Hanley et al. 2003).

Although ENSO is a phenomenon in the tropical Pacific, its impacts reach manyregions far from the Pacific. One commonly accepted and widely mentioned im-pact is that El Nino suppresses and La Nina enhances the TC activities (seasonalhurricane days and hurricane numbers) in the Atlantic basin (Gray 1984; Stevenson2012; Patricola et al. 2015). The major mechanism for the ENSO role in theAtlantic TC activities is that ENSO changes one of the most important factorsaffecting TC development, the unfavorable vertical wind shear. El Nino leads toincreased vertical wind shear in the Atlantic while La Nina decreases the shear(Gray 1984; Patricola et al. 2015).

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 2

Because of the important role of ENSO in Atlantic TC activities, mostseasonal prediction schemes of the Atlantic TC activities use ENSO compo-nents as one of the predictors (Klotzbach 2007, 2011; Davis et al. 2015). Mostdiscussions on the trend changes of TC activities in perspective of climatechange is also in the context of ENSO and/or SST changes (Goldenberg et al.2001; Webster et al. 2005; Emanuel 2005; Mann et al. 2009). Nevertheless, theENSO impact on the Atlantic TC activities is not uniform for long time periods.For example, Gray (1984) did not find the same relationship between ENSOand Atlantic TC activities for data covering the nineteenth century and attri-butes that to data quality. Additionally, different ENSO influences on TC ac-tivities for below-normal and above-normal periods have been identified, and itwas demonstrated that the ENSO impacts can be masked by multidecadalsignals (Bell and Chelliah 2006). Other studies modulate the ENSO impactbased on the phases of the Atlantic multidecadal oscillation (AMO; Davis et al.2015).

Since the ENSO impact on the Atlantic TC activities is time dependent, theimpact itself may show certain variation. What is the quantitative description ofthis impact as a function of time? Are there any patterns in the impact variationwith time? What causes this pattern? Here, we evaluate the historical ENSO dataand its impact on the Atlantic TC activity (defined by number of named storms) inan innovative way to assess how the impact changes over time on a centennial timescale and what possibly causes the variation. The results are potentially helpful forlong-term prediction of the Atlantic TC activity beyond seasonal and decadalscales.

2. Datasets and methodsSeveral datasets are utilized for this study. The data of cold and warm epi-

sodes are given by the CPC of the U.S. National Oceanic and AtmosphericAdministration (NOAA) from 1950 to present based on the monthly SSTA inthe Nino-3.4 region (CPC 2015). Although the CPC definition is the commonlyaccepted standard for El Nino and La Nina definitions, the covered time periodis too short for the intended analysis of long-term ENSO impact on the AtlanticTC activity. To have a longer time series, the commonly used alternative SOIdataset, from NOAA Earth System Research Laboratory (ESRL), is also used.These data cover the period from 1866 to present with monthly temporal res-olution (ESRL 2015). The TC activity dataset is the NOAA Atlantic hurricanedatabase (HURDAT) from 1851 to present, which records the number of namedstorms, hurricanes, major hurricanes, and the accumulated cyclone energy(ACE) for each year [Landsea et al. 2010; Hurricane Research Division (HRD)2015]. As is widely known, the number of records for earlier years before theavailability of the satellite observations is lower than the reality (Landsea andFranklin 2013). However, the main intent of using this data is to compare thedifference in similar time periods, and the undercounting will not be an issuefor this purpose (Klotzbach 2011). In this study, the cutoff time is December2014.

The first task for utilizing a long time series is to use SOI data to define thewarm and cold episodes that are close to those defined by SSTA. Simply using

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 3

threshold values to define warm and cold episodes based on SOI results in a largediscrepancy between the SOI-based definition and the definition by SSTA nomatter how the threshold values are adjusted. To improve the agreement of thetwo definitions, we mimic the SSTA treatment by using a 3-month moving av-erage and at least five consecutive-averaged SOI values lower (higher) than thewarm (cold) SOI threshold value to define a warm (cold) episode. The SOI valuesare first filtered with a 3-month moving average. The smoothed data are checkedwith a warm (cold) threshold value. If there are at least five consecutive-averagedSOI values smaller (larger) than the warm (cold) threshold value, it is concludedthat there is a warm (cold) episode. If the August–October (ASO)-averaged SOI isin the warm (cold) episode, the corresponding year is defined as a warm (cold)season.

With the long-term warm and cold episodes defined with SOI values, thenumbers of named storms (tropical storms, hurricanes, and subtropical storms;HRD 2015) in HURDAT are counted for warm and cold episodes. Basic statisticsincluding differencing, moving average, correlation, and Fourier analysis are ap-plied to the counted numbers.

3. Results

3.1. Long-term warm and cold episodes defined with SOI

Unlike the fixed SSTA threshold values, 60.58C used by CPC (2015) in thewarm/cold episode definition, the threshold values mentioned above for SOI areadjusted to search for the best results, the least discrepancy between the SOI-basedepisodes and the SSTA-based episodes. This research shows that the discrepancy issensitive for the cold threshold. The best result is obtained when the thresholdvalue is 0.42. That is, if the ASO-averaged SOI is higher than 0.42 within fiveconsecutive such ‘‘high’’ months, that season is defined as the cold season. Withthis definition, one can find that in the SOI and SSTA overlapping period 1950–2014, there are 3 years being identified as cold years with SOI but not with SSTA.On the other extreme, 4 years are missed, and this gives a total seven mismatchesfor cold seasons. On the contrary, the impact of the warm threshold values is muchmore stable. With all values between20.54 and20.39, the outcomes are the same.When the threshold value is set to be20.39, there are two extra warm seasons andthree missed seasons with a total of five mismatches. When the threshold valueschange between20.54 and20.39, the specific mismatched years may be differentbut the total number of mismatches remains at 5.

Figure 1 displays the time series of ASO SOI values and the warm (cold) seasonsdefined above with 20.39 (0.42) as the warm (cold) threshold value. One can seethat there are roughly seven warm seasons in 20 years, consistent with the 2–7-yrperiod of the ENSO phenomenon. From this figure, one can easily identify themisses or ‘‘false hits’’ when compared to the SSTA-based warm or cold seasonsafter the 1950 season. For example, the two open circles represent the two seasons,1992 and 1993, which are identified as warm seasons by SOI but not by SSTA.These are the false hits. On the other hand, the three crosses without circles,representing 1968, 1976, and 1986, denote warm seasons identified by SSTA butfailed to be selected by SOI. These are the misses. All other warm seasons are

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 4

detected by both SOI and SSTA. The detection of cold seasons has a relativelylarge difference with four misses (open diamonds for 1954, 1995, 1999, and 2007)and three false hits (unenclosed X symbols for 1956, 1974, and 2008).

3.2. Variation of the ENSO impacts

With warm and cold episodes defined based on SOI values, long-term counts ofnamed storms for each category of the episodes become possible. Here, a 31-yrmoving average is used to smooth the results. That is, 31 years are selected to be thetime period, and the mean counts of named storms in all warm seasons and all coldseasons during the selected 31 years are calculated.

Figure 2 gives the 31-yr moving average counts of named storms in the Atlanticin warm and cold seasons. For example, in the 31-yr period 1866–96, denoted bythe midyear 1881, the average number of named storms is 6.7 in warm years and9.8 in cold years. Since the 1-yr shift of the 31-yr window may not result in anychanges in the members of warm and cold groups, there are many short flat seg-ments in the counts of the named storms. This figure clearly demonstrates thecommonly accepted relationship between ENSO and TC activity in the Atlantic,cold episodes being favorable to TCs and warm episodes depressing TCs as thecold average is always higher than the warm average. However, this relationship isonly significant for certain periods such as the most recent period starting in the1980s. In the middle part of the whole study time range, from the early 1920s tolate 1970s, although the difference is evident with cold counts being larger than thewarm counts, it is difficult to say the difference is substantial. To quantify thedifferences, the one-tail t test for two means assuming unequal variances is carried

Figure 1. SOI values with warm and cold seasons. The SOI values displayed are the3-month moving average value at September (ASO) for each year. Thevertical dashed line is the start year (1950) with Nino-3.4 SSTA definedwarm and cold episodes. The dashed horizontal is the maximum thresholdvalues (20.39) for defining the SOI-based warm episodes, and the dottedhorizontal line is the minimum value (0.42) for defining the cold episode.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 5

out for each period. As expected, the difference is significant at a 0.05 significancelevel before 1902 and after 1984 only. In between, the p values (not shown) arelarger than 0.05. If the significance is set at 0.1, the middle insignificant period willbe reduced from 1902 to 1984 to 1908 to 1982 with two subperiods, 1950–53 and1978–80, as exceptions.

In addition to the warm and cold mean counts in Figure 2, the difference betweenthe cold means and the warm means [cold warm difference (CWD)] is also dis-played. This shows that the difference changes in a very low frequency, and alongthe large-scale variation, there are relative high-frequency but small-amplitudeoscillations. The entire dataset seems to cover only one oscillation period, from1881 to 1999, giving an oscillation with the longest period of 118 years. Whenlonger time series of TC activity are available, this variation should be revisitedcarefully. Figure 3 shows spectral power based on Fourier analysis of the CWDvalues and confirms the observation. The Fourier components are converted intopercentage of the total power in the plot. The dominant component with the 118-yrperiod has 58.8% of all power, and the component with the second (third) highestpower is the one with the 39.3-yr (19.7 yr) period and 11.9% (6.40%) of the power.There are only five other components with larger than 1% of the total power but allless than 4%.

3.3. Driving factor(s)

What causes the low-frequency changes of ENSO impact to the Atlantic TCactivity? First, we turned to well-known existing climate indices. Many climateindices are created to represent various types of climate variability. For example,ESRL listed many such indices from popular SOI and North Atlantic Oscillation

Figure 2. Average counts of named storms in warm and cold seasons and theirdifferences with a 31-yr moving window. The year in abscissa is themiddle year of the 31-yr period. The cold/warm difference is shifted by 6(with CWD6 5 cold number 2 warm number 1 6) for display purpose.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 6

(NAO) to regional specific northeast Brazil rainfall anomaly to even solar flux(ESRL 2017). Many natural phenomena and their changes including TC activ-ities are correlated to those climate indices (Goldenberg et al. 2001; Cobb et al.2013). Most of these indices demonstrate variability in seasonal to interannual ordecadal scales. Commonly mentioned climate indices with low frequency fromdecadal to centennial scales include AMO (Enfield et al. 2001), NAO (Hurrell1995; Jones et al. 1997), Pacific decadal oscillation (PDO; Zhang et al. 1997),and the tripole index for the interdecadal Pacific oscillation (TPI; Henley et al.2015), and those indices are related to the Atlantic TC activity. For example,Biondi et al. (2001) used tree-ring data to investigate the PDO variability andidentified a bidecadal mode. Interestingly, the dominant negative phase identifiedby their first principal component (1945–70 in their Figure 3) is partially coin-cident with the lowest TC count difference between the warm and cold episodes(1918–71 by using 2 as the threshold for the count difference except for a sub-period 1948–54), but the overall trends in the nearby periods are quite different.Moreover, no single index, among those commonly studied, actually showsvariability with such a low frequency for a period longer than a century.

Climate indicators with a longer time scale also exist. Minobe (1997) inves-tigated instrumental spring air temperature in western North America, winter–spring sea level pressure (SLP) in the central North Pacific and SST in variousregions and found climate regime shifts in the 1940s and 1970s. Earlier shifts alsoappear in some but not all of the data. Using the multitaper method (MTM), Minobe(1997) found the periods of those time series are between 50 to 70 years. Further-more, Minobe (1997) used the tree-ring-based constructed data in North America toconfirm the findings in regime shifts and the 50–70 oscillation periods with the firstEOF mode.

The climate index with the lowest frequency other than those on proxy dataof paleoclimate scales in literature is possibly the Southern Ocean centennial

Figure 3. Spectral power of CWD. The power values displayed are normalized tomake the total power be one.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 7

variability (SOCV) index identified by Latif et al. (2013). SOCV, representingthe centennial-scale internal variability in the Southern Ocean, is based on theSouthern Ocean SSTanomaly averaged over the latitude band 508–708S. Latif et al.(2013) attribute the centennial variability to deep-ocean convection and link Arcticand Antarctic sea ice extent phase discrepancy, recent slowing of mean global airtemperature increase, and even the change of the southern annular mode (SAM) toSOCV. The SOCV shows a 100-yr period (Figure 2a in Latif et al. 2013), althoughthe simulation results and tree-ring-based proxy data indicate a period rangingfrom 200 to 500 years.

Because of the similar periodicities between SOCVand the TC count differencein warm and cold eastern Pacific episodes, it is hypothesized that the two phe-nomena are correlated to each other. To quantify the relationship, correlationanalysis is conducted between the count difference and SOCV as well as otherclimate indices mentioned above. SOI is also included in the correlation analysis,although it actually shows mainly seasonal to interannual variability. Initially,Pearson’s r correlation was employed. However, the Shapiro–Wilks test resultsshow that the CWD values do not follow a normal distribution. Therefore,Spearman’s rank correlation is utilized in this study.

The correlation coefficient values between the count differences and other in-dices are listed in Table 1 with the corresponding p values. The concurrent cor-relation values show only AMO, PDO, and SOCV are significantly (at 0.05significance level) correlated with the count difference, and the correlation forAMO and PDO are only around 0.2 (absolute value). Since the count is a low-frequency signal, the impact from other climate phenomena may not be concurrent.As a result, we also calculate the correlation with time lags [CWD(t) vs Index(t1 lag)]and identify the largest correlation with a specific lag. Those maximum correlationvalues and the associated time lags in years are also shown in Table 1. Except forSOI, all other indices demonstrate significant correlations at various lags. Theinitial maximum time lag is set at 15 years since the count is calculated in durationswith 15 years as its half-length. If that maximum lag is increased to 20, the onlychange to the maximum correlation is that of the NAO from 0.13 to20.19 with lagchanges from lag 23 to lag 220.

The correlation results can also be demonstrated via the time series plots. Figure 4adisplays CWD and SOCV values with the annual average values of other climateindices. To make the values in a similar range, the CWD values are normalized bytaking zero mean and scaling by 3 times the standard deviation. Except for theoriginal annual-based values of CWD and SOCV, oscillations with decadal ormultidecadal scales are dominant. However, the oscillations of relatively highfrequencies make it very difficult to see the relationships among those time series.To have a clearer picture of the impacts of the climate indices on the count dif-ference, a moving average with the same time interval, 31 years as for CWD, isutilized. The results with a moving average are displayed in Figure 4b, and onecan see the simple correlation relationship much easier with the smoothed datathan with the original annual values. For example, the trend of PDO (green line) iscoincident with the trend of CWD since around 1950. This coincidence cannotbe identified with the unsmoothed data. After smoothing, lag 21 results in thehighest correlation. The negative 1-yr lag means that we should shift the PDOright by 1 year to make the two time series coincident. The curves after 1950 in

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 8

Figure 4b clearly demonstrate this effect. In this case, the highest correlationbetween CWD and PDO is 0.47, higher than the 0.34 achieved without smoothing atlag 7 (Table 1).

The correlation improving with smoothed data is not a surprise. All correlationcoefficients with the moving average values are higher (in absolute values) than thosewith the original annual values because the smoothing effects filter out the high-frequency oscillations and introduce/enhance autocorrelation. It is interesting to notethat SOI results in higher correlation than SOCV after smoothing. However, themaximum correlations with optimal lags show that a leading SOCV with 218 lagleads to the highest correlation, as high as 0.83, among all indices. The correlationstrength with SOI increases less than that for SOCV from 0.53 (negative) to 0.58(negative) with lag22. Nevertheless, the correlation with SOI is possibly an artifact.Before the smoothing, the SOI oscillates around zero with relatively high frequencies,and the amplitude is large. The smoothed SOI is of small amplitude in the oscillation

Table 1. Correlation values between CWD and climate indices. The second andthird columns are values of concurrent correlation coefficient (cc) and the corre-sponding p values. The fourth and fifth columns are for the maximum cc valuesamong all lags with the Maxlag limit and the corresponding p values. The lastcolumn is the year shift of the climate indices for achieving the maximum cc.

Index cc p value Max cc p value Lag

Original data: Maxlag 5 15AMO 22.0 3 1021 3.0 3 1022 22.7 3 1021 2.83 1023 26NAO 1.1 3 1021 2.4 3 1021 1.3 3 1021 1.4 3 1021 23PDO 2.1 3 1021 3.3 3 1022 3.4 3 102101 3.0 3 1024 7SOI 21.1 3 1021 2.3 3 1021 21.6 3 1021 8.1 3 1022 24TPI 1.2 3 1021 1.9 3 1021 2.3 3 1021 1.1 3 1022 7SOCV 4.3 3 1021 1.1 3 1026 7.5 3 1021 1.4 3 10222 214

Original data: Maxlag 5 20

AMO 22.0 3 1021 3.03 1022 22.7 3 1021 2.8 3 1023 26NAO 1.1 3 1021 2.4 3 1021 21.9 3 1021 4.0 3 1022 220PDO 2.1 3 1021 3.3 3 1022 3.4 3 1021 3.03 1024 7SOI 21.1 3 1021 2.3 3 1021 21.6 3 1021 8.1 3 1022 24TPI 1.2 3 1021 1.9 3 1021 2.3 3 1021 1.1 3 1022 7SOCV 4.3 3 1021 1.1 3 1026 7.5 3 1021 1.4 3 10222 214

With MA: Maxlag 5 15

AMO 22.5 3 1021 5.3 3 1023 22.7 3 1021 2.7 3 1023 25NAO 1.7 3 1021 5.7 3 1022 4.4 3 1021 2.9 3 1026 15PDO 4.6 3 1021 8.6 3 1026 4.7 3 1021 8.3 3 1026 21SOI 25.3 3 1021 4.3 3 10210 25.8 3 1021 6.2 3 10212 22TPI 3.7 3 1021 4.5 3 1025 5.4 3 1021 2.6 3 10210 15SOCV 5.1 3 1021 2.2 3 1029 8.0 3 1021 2.0 3 10227 215

With MA: Maxlag 5 20

AMO 22.5 3 1021 5.3 3 1023 22.7 3 1021 2.7 3 1023 25NAO 1.7 3 1021 5.7 3 1022 4.8 3 1021 4.0 3 1027 20PDO 4.6 3 1021 8.6 3 1026 4.7 3 1021 8.3 3 1026 21SOI 25.3 3 1021 4.3 3 10210 25.8 3 1021 6.2 3 10212 22TPI 3.7 3 1021 4.5 3 1025 5.6 3 1021 2.5 3 10211 18SOCV 5.1 3 1021 2.2 3 1029 8.3 3 1021 1.8 3 10230 218

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 9

Figure 4. Time series of normalized CWD (with three std dev) and other climate in-dices. (a) Indices with original temporal annual resolution. (b) Smoothedindex values with 31-yr moving average. (c) CWD and SOCV only. Bothoriginal annual SOCVand the smoothed SOCVare included. The SOCVareshifted right 18 years in (c).

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 10

except for the most recent 30 years and is of relatively high frequency. So, the highcorrelation is mainly due to the small-amplitude oscillations in the smoothedSOI, which does not make much sense. In contrast, the variability of the smoothedPDO is more prominent because of its high amplitude and low frequency, illustratingsimilar variability compared with CWD. Nevertheless, the amplitude modulation ofSOI does increase and is consistent with the large ENSO impact in most recent years(Li et al. 2011; Burn and Palmer 2015).

With that said, the most interesting correlation result is that with SOCV, whichnot only gives the highest value but also demonstrates consistency with the lag.With the original annual SOCV values, the correlation is 0.43, which is muchhigher than the correlation with all other climate indices. After smoothing, thecorrelation is increased to 0.51, which is not much gain because the original SOCVis mainly for low-frequency variability, and it is much smoother than other cli-mate indices. In searching for the optimal lag with the original data, the maximumcorrelation is 0.75 with a214 lag. Similarly, the largest correlation detected fromthe smoothed data is 0.80 at 215 lag when the maximum lag is limited to 15.Since the lag is on the preset limit, the maximum lag is extended to 20, and theoptimal lag is pinpointed at 218, at which the correlation is 0.83. If we use theconcept of coefficient of determination R2, we can say that SOCV can explain69% change in CWDwith an 18-yr lag. More importantly, the negative lag meansthat SOCV leads the CWD, and the change of SOCV can be used for the predictionof CWD and therefore the TC activity in the Atlantic. To see the lag impact, theSOCV is shifted by 18 years to the right and the result is displayed in Figure 4c. Thisfigure exhibits strong consistent variability between CWD and SOCV.

Climate model simulations suggest the SOCV is generated in the SouthernOcean. Its mechanism is distinct from other longer time-scale variability, such asAMO. Park and Latif (2008) argue that the multidecadal variability in the At-lantic sector is generated in the North Atlantic, while the SOCV is driven in theSouthern Ocean where sea ice change is considerably involved. Martin et al.(2013) further show that building up of heat in the middepth and its release to theatmosphere is a key to generate the SOCV. The source of heat is inflow of the NorthAtlantic DeepWater (NADW), which indicates a large-scale link between the Northernand Southern Hemispheres. The SOCV signal generated in the South Atlanticalso propagates to the North Atlantic via different processes. Swingedouw et al.(2009) proposed three ways of connections: deep-water adjustment via oceanicwaves, salinity anomaly advection, and wind impact on the NADW cell. Eachhas different response time scales in the model, of which the precise time scalemay differ in the observation.

The physical link between the SOCV and TCs may be explained with one or acombination of these propagation dynamics. One strong candidate is the advectiveprocess, as illustrated by climate models (Vellinga and Wu 2004; Menary et al.2012). In the advective processes, a strengthened NADW cell, or Atlantic merid-ional overturning circulation (MOC), linked to the SOCV, transports more heat tothe Northern Hemisphere from the Southern Hemisphere and increases SST overthe tropical and subtropical Atlantic, where development of TCs is active.

It must be pointed out that the correlation analysis above really shows that thevariation in CWD is of long time scale, and its correlation with commonly usedclimate indices is weak. The correlation with SOCV is the only one showing a

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 11

strong relationship. Because any two time series of similar periods would result inhigh-correlation values because of the strong autocorrelation, the impact of theautocorrelation on the significance of the correlation is estimated. We used thewidely accepted correction formula (Bretherton et al. 1999; McCarthy et al. 2015)

neff 5 n12 a1a211 a1a2

,

where n is the number of observations of time series and a1 and a2 are thelag-1 autocorrelation of each of the two time series, respectively, and find that theeffective size of the data neff for the correlation between CWD and SOCV isreduced from 119 to 9.25. Nevertheless, the lagged correlation is so strong thateven after the correction, the p value is still less than 0.05.

4. Conclusions and discussionThis work demonstrates how a centennial cycle of ENSO impacts the Atlantic TC

activity and how this century-scale variation can be plausibly linked to the SOCV. Tothe best knowledge of the authors, this is the first study to reveal TC-related variationin centennial scale with nonproxy data, although this variation is not directly on TCactivities. This work sheds light on two aspects of long-term TC prediction. One ofthem is on seasonal prediction. Right now, there are many efforts focused on seasonalpredictions for the Atlantic hurricane activities, such as those by Colorado StateUniversity, the NOAA CPC, and the private British forecasting firm Tropical StormRisk (http://www.tropicalstormrisk.com/forecasts.html). Among them, ENSO is animportant predictor. The work showed above clearly reveals that the ENSO impacton the Atlantic TC activity is complicated, and the ENSO factor in the seasonalprediction should be modulated. An example of such work is the conditional ENSOimpact on seasonal Atlantic TC prediction by Davis et al. (2015) in which the ENSOcontribution is conditioned by AMO. This work suggests long-term variation such asSOCV could be used for the condition.

Another motivation for this work is for enhancing longer than seasonal TCprediction. Multiyear TC prediction should be more useful for insurance purposesand long-term disaster mitigation. Similar topics are investigated in several studies(e.g., Vecchi et al. 2013; Smith et al. 2010). Vecchi et al. (2013) investigated themultiyear prediction of TC frequency in the North Atlantic with climate models forSSTs and a statistical emulator for TC frequency, which is a function of someaggregated SSTs (Vecchi et al. 2011). As in other studies on long-term TC activitychanges, the majority of the work is based on outputs from climate models, and thefocus is on long-term climate changes as well as the associated changes in theAtlantic TC activities (e.g., Dunstone et al. 2011; Dunstone et al. 2013: Caron et al.2014; Caron et al. 2015). This study and the corresponding SOCV data could beused for even longer-term forecasting of the Atlantic TC activity.

Acknowledgments. We thank one of anonymous reviewers for pointing out the datadistribution issue, which leads to the change from Pearson’s r to Spearman’s rank correlationanalysis. Publication of this article was funded in part by the George Mason UniversityLibraries Open Access Publishing Fund.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 12

References

Bell, G. D., and M. Chelliah, 2006: Leading tropical modes associated with interannual andmultidecadal fluctuations in North Atlantic hurricane activity. J. Climate, 19, 590–612,https://doi.org/10.1175/JCLI3659.1.

Biondi, F., A. Gershunov, and D. R. Cayan, 2001: North Pacific decadal climate variability since1661. J. Climate, 14, 5–10, https://doi.org/10.1175/1520-0442(2001)014,0005:NPDCVS.2.0.CO;2.

Bretherton, C. S., M. Widmann, V. P. Dymnikov, J. M. Wallace, and I. Blade, 1999: The effectivenumber of spatial degrees of freedom of a time-varying field. J. Climate, 12, 1990–2009,https://doi.org/10.1175/1520-0442(1999)012,1990:TENOSD.2.0.CO;2.

Burn, M. J., and S. E. Palmer, 2015: Atlantic hurricane activity during the last millennium. Sci.Rep., 5, 12838, https://doi.org/10.1038/srep12838.

Caron, L.-P., C. G. Jones, and F. Doblas-Reyes, 2014: Multi-year prediction skill of Atlantichurricane activity in CMIP5 decadal hindcasts. Climate Dyn., 42, 2675–2690, https://doi.org/10.1007/s00382-013-1773-1.

——, L. Hermanson, and F. J. Doblas-Reyes, 2015: Multiannual forecasts of Atlantic U.S. tropicalcyclone wind damage potential. Geophys. Res. Lett., 42, 2417–2425, https://doi.org/10.1002/2015GL063303.

Cobb, K. M., N. Westphal, H. R. Sayani, J. T. Watson, E. D. Lorenzo, H. Cheng, R. L. Edwards, andC. D. Charles, 2013: Highly variable El Nino–Southern Oscillation throughout the Holocene.Science, 339, 67–70, https://doi.org/10.1126/science.1228246.

CPC, 2015: Cold and warm episodes by season. Accessed 28 December 2015, http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml.

Davis, K., X. Zeng, and E. A. Ritchie, 2015: A new statistical model for predicting seasonal North Atlantichurricane activity. Wea. Forecasting, 30, 730–741, https://doi.org/10.1175/WAF-D-14-00156.1.

Dunstone, N. J., D. M. Smith, and R. Eade, 2011: Multi-year predictability of the tropical Atlanticatmosphere driven by the high latitude North Atlantic Ocean. Geophys. Res. Lett., 38,L14701, https://doi.org/10.1029/2011GL047949.

——, ——, B. B. B. Booth, L. Hermanson, and R. Eade, 2013: Anthropogenic aerosol forcing ofAtlantic tropical storms. Nat. Geosci., 6, 534–539, https://doi.org/10.1038/ngeo1854.

Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature,436, 686–688, https://doi.org/10.1038/nature03906.

Enfield, D. B., A. M. Mestas-Nunez, and P. J. Trimble, 2001: The Atlantic multidecadal oscillationand its relation to rainfall and river flows in the continental U.S. Geophys. Res. Lett., 28,2077–2080, https://doi.org/10.1029/2000GL012745.

ESRL, 2015: Southern Oscillation index (SOI). Accessed 28December 2015, http://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/Data/soi.long.data.

——, 2017: Climate indices: Monthly atmospheric and ocean time series. Accessed 5 July 2017,https://www.esrl.noaa.gov/psd/data/climateindices/list/.

Goldenberg, S. B., C. W. Landsea, A. M. Mestas-Nunez, and W. M. Gray, 2001: The recent increasein Atlantic hurricane activity: Causes and implications. Science, 293, 474–479, https://doi.org/10.1126/science.1060040.

Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part I: El Nino and 30mb quasi-biennial oscillation influences. Mon. Wea. Rev., 112, 1649–1668, https://doi.org/10.1175/1520-0493(1984)112,1649:ASHFPI.2.0.CO;2.

Hanley, D. E.,M. A. Bourassa, J. J. O’Brien, S. R. Smith, and E. R. Spade, 2003: A quantitative evaluationof ENSO indices. J. Climate, 16, 1249–1258, https://doi.org/10.1175/1520-0442(2003)16,1249:AQEOEI.2.0.CO;2.

Henley, B. J., J. Gergis, D. J. Karoly, S. B. Power, J. Kennedy, and C. K. Folland, 2015: A tripoleindex for the interdecadal Pacific oscillation. Climate Dyn., 45, 3077–3090, https://doi.org/10.1007/s00382-015-2525-1.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 13

HRD, 2015: Frequently asked questions. Accessed 28 December 2015, http://www.aoml.noaa.gov/hrd/tcfaq/E11.html.

Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation and relationships to re-gional temperature and precipitation. Science, 269, 676–679, https://doi.org/10.1126/science.269.5224.676.

Jones, P. D., T. Jonsson, and D. Wheeler, 1997: Extension to the North Atlantic Oscillation usingearly instrumental pressure observations from Gibraltar and south-west Iceland. Int. J. Cli-matol., 17, 1433–1450, https://doi.org/10.1002/(SICI)1097-0088(19971115)17:13,1433::AID-JOC203.3.0.CO;2-P.

Klotzbach, P. J., 2007: Revised prediction of seasonal Atlantic basin tropical cyclone activity from1 August. Wea. Forecasting, 22, 937–949, https://doi.org/10.1175/WAF1045.1.

——, 2011: A simplified Atlantic basin seasonal hurricane prediction scheme from 1 August.Geophys. Res. Lett., 38, L16710, https://doi.org/10.1029/2011GL048603.

Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presen-tation of a new database format. Mon. Wea. Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1.

——, G. A. Vecchi, L. Bengtsson, and T. R. Knutson, 2010: Impact of duration thresholds onAtlantic tropical cyclone counts. J. Climate, 23, 2508–2519, https://doi.org/10.1175/2009JCLI3034.1.

Latif, M., T. Martin, and W. Park, 2013: Southern Ocean sector centennial climate variability andrecent decadal trends. J. Climate, 26, 7767–7782, https://doi.org/10.1175/JCLI-D-12-00281.1.

Li, J., S.-P. Xie, E. R. Cook, G. Huang, R. D’Arrigo, F. Liu, J. Ma, and X.-T. Zheng, 2011:Interdecadal modulation of El Nino amplitude during the past millennium. Nat. ClimateChange, 1, 114–118, https://doi.org/10.1038/nclimate1086.

Mann, M. E., J. D. Woodruff, J. P. Donnelly, and Z. Zhang, 2009: Atlantic hurricanes and climateover the past 1,500 years. Nature, 460, 880–883, https://doi.org/10.1038/nature08219.

Martin, T., W. Park, and M. Latif, 2013: Multi-centennial variability controlled by Southern Oceanconvection in the Kiel Climate Model. Climate Dyn., 40, 2005–2022, https://doi.org/10.1007/s00382-012-1586-7.

McCarthy, G. D., I. D. Haigh, J. J. Hirschi, J. P. Grist, and D. A. Smeed, 2015: Ocean impact ondecadal Atlantic climate variability revealed by sea-level observations. Nature, 521, 508–510,https://doi.org/10.1038/nature14491.

Menary, M. B., W. Park, K. Lohmann, M. Vellinga, M. D. Palmer, M. Latif, and J. H. Jungclaus,2012: A multimodel comparison of centennial Atlantic meridional overturning circulationvariability. Climate Dyn., 38, 2377–2388, https://doi.org/10.1007/s00382-011-1172-4.

Minobe, S., 1997: A 50–70 year climatic oscillation over the North Pacific and North America.Geophys. Res. Lett., 24, 683–686, https://doi.org/10.1029/97GL00504.

Park, W., and M. Latif, 2008: Multidecadal and multicentennial variability of the meridionaloverturning circulation. Geophys. Res. Lett., 35, L22703, https://doi.org/10.1029/2008GL035779.

Patricola, C. M., P. Chang, and R. Saravanan, 2015: Degree of simulated suppression of Atlantictropical cyclones modulated by flavour of El Nino. Nat. Geosci., 9, 155–160, https://doi.org/10.1038/ngeo2624.

Philander, S. G., 1990: El Nino, La Nina, and the Southern Oscillation. Academic Press, 293 pp.Smith, D. M., R. Eade, N. Dunstone, D. Fereday, J. M. Murphy, H. Pohlmann, and A. Scaife, 2010:

Skillful multi-year predictions of Atlantic hurricane frequency. Nat. Geosci., 3, 846–849,https://doi.org/10.1038/ngeo1004.

Stevenson, S., 2012: Significant changes to ENSO strength and impacts in the twenty-first century:Results from CMIP5.Geophys. Res. Lett., 39, L17703, https://doi.org/10.1029/2012GL052759.

Swingedouw, D., T. Fichefet, H. Goosse, and M. F. Loutre, 2009: Impact of transient freshwaterreleases in the Southern Ocean on the AMOC and climate. Climate Dyn., 33, 365–381,https://doi.org/10.1007/s00382-008-0496-1.

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 14

Vecchi, G. A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I. M. Held, and R. Gudgel,2011: Statistical–dynamical predictions of seasonal North Atlantic hurricane activity. Mon.Wea. Rev., 139, 1070–1082, https://doi.org/10.1175/2010MWR3499.1.

——, and Coauthors, 2013: Multiyear predictions of North Atlantic hurricane frequency: Promiseand limitations. J. Climate, 26, 5337–5357, https://doi.org/10.1175/JCLI-D-12-00464.1.

Vellinga, M., and P. L. Wu, 2004: Low-latitude freshwater influence on centennial variability of theAtlantic thermohaline circulation. J. Climate, 17, 4498–4511, https://doi.org/10.1175/3219.1.

Webster, P., G. Holland, J. Curry, and H. Chang, 2005: Changes in tropical cyclone number,duration, and intensity in a warming environment. Science, 309, 1844–1846, https://doi.org/10.1126/science.1116448.

Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: 1900–93.J. Climate, 10, 1004–1020, https://doi.org/10.1175/1520-0442(1997)010,1004:ELIV.2.0.CO;2.

Earth Interactions is published jointly by the American Meteorological Society, the American Geophysical

Union, and the Association of American Geographers. For information regarding reuse of this content and

general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Earth Interactions d Volume 22 (2018) d Paper No. 1 d Page 15